{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6e8ecbaa-a6bc-4433-a0bf-81aa7632d4f8",
   "metadata": {},
   "source": [
    "# <span style=\"color: #3498db;\">一元线性回归</span>\r\n",
    "\r\n",
    "## 案例1实战：工作年限与收入的线性回归模型\r\n",
    "\r\n",
    "案例背景：\n",
    "通常来说，收入都会随着工作年限的增长而增长，而在不同的行业中收入的增长速度都会有所不同，本小节就是来通过一元线性回归模型来探寻工作年限对收入的影响，也即搭建收入预测模型，同时比较多个行业的收入预测模型来分析各个行业的特点。\r\n",
    "建模。\r\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69b97a96-ecf5-45e8-8847-b2f1b6d99012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>工龄</th>\n",
       "      <th>薪水</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>13611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>12306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>12151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>13057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    工龄     薪水\n",
       "0  0.0  10808\n",
       "1  0.1  13611\n",
       "2  0.2  12306\n",
       "3  0.3  12151\n",
       "4  0.3  13057"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a=pd.read_excel('IT行业收入表.xlsx')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bade85e7-4b1e-4b26-9580-44a04d4884d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=a[['工龄']]\n",
    "y=a[['薪水']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1b83a3e-d9a5-497e-8537-b54eaed4a41d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dda6d442-f08d-4ab0-866c-dd1357eb6da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.scatter(x,y,c='red')\n",
    "plt.xlabel('工龄')\n",
    "plt.ylabel('薪水')\n",
    "plt.title('工作年限对收入的影响')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63b84241-f635-41a2-bfc3-3bb772faf50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "b=LinearRegression()\n",
    "b.fit(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1c1be4-f015-43fc-abe5-adccc4fa788d",
   "metadata": {},
   "source": [
    "模型的可视化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "084daaac-f1f2-4d4b-a668-f2aeb60f5e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color='green')\n",
    "plt.plot(x,b.predict(x),color='red')\n",
    "plt.xlabel('工龄')\n",
    "plt.ylabel('薪水')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142d850b-abd4-4ced-a8a2-b7c6ad41e328",
   "metadata": {},
   "source": [
    "构造线性回归方程："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "004ea440-eb0d-47bd-9e4b-f94d798ea324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数k为：[2497.1513476]\n",
      "截距b为：[10143.13196687]\n"
     ]
    }
   ],
   "source": [
    "print('系数k为：'+str(b.coef_[0]))\n",
    "print('截距b为：'+str(b.intercept_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfa71e1-1f63-4009-a54f-b228d6cc292a",
   "metadata": {},
   "source": [
    "此时的一元线性回归曲线方程就是：y=2497*x+10143"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a220d6-7b7f-40af-9665-ed9f345e9034",
   "metadata": {},
   "source": [
    "### 我进行了五种不同算法或模型的分析和对比代码\n",
    "包括**多项式回归（三次）、决策树回归、随机森林、支持向量机、递归提升回归**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bde5bf2-b469-4ef2-af75-3df9974b9c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-3.0.5-py3-none-win_amd64.whl.metadata (2.1 kB)\n",
      "Requirement already satisfied: numpy in d:\\program files\\anaconda3\\lib\\site-packages (from xgboost) (1.26.4)\n",
      "Requirement already satisfied: scipy in d:\\program files\\anaconda3\\lib\\site-packages (from xgboost) (1.15.3)\n",
      "Downloading xgboost-3.0.5-py3-none-win_amd64.whl (56.8 MB)\n",
      "   ---------------------------------------- 0.0/56.8 MB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/56.8 MB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/56.8 MB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/56.8 MB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/56.8 MB 217.9 kB/s eta 0:04:21\n",
      "   ---------------------------------------- 0.1/56.8 MB 297.7 kB/s eta 0:03:11\n",
      "   ---------------------------------------- 0.1/56.8 MB 467.6 kB/s eta 0:02:02\n",
      "   ---------------------------------------- 0.1/56.8 MB 450.6 kB/s eta 0:02:06\n",
      "   ---------------------------------------- 0.2/56.8 MB 719.7 kB/s eta 0:01:19\n",
      "   ---------------------------------------- 0.3/56.8 MB 770.1 kB/s eta 0:01:14\n",
      "   ---------------------------------------- 0.5/56.8 MB 1.2 MB/s eta 0:00:47\n",
      "   ---------------------------------------- 0.6/56.8 MB 1.3 MB/s eta 0:00:45\n",
      "    --------------------------------------- 1.0/56.8 MB 2.0 MB/s eta 0:00:28\n",
      "    --------------------------------------- 1.3/56.8 MB 2.3 MB/s eta 0:00:25\n",
      "   - -------------------------------------- 1.6/56.8 MB 2.9 MB/s eta 0:00:19\n",
      "   - -------------------------------------- 2.3/56.8 MB 3.6 MB/s eta 0:00:16\n",
      "   - -------------------------------------- 2.5/56.8 MB 3.8 MB/s eta 0:00:15\n",
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      "   ---------------------------------------- 56.8/56.8 MB 9.3 MB/s eta 0:00:00\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-3.0.5\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cbe8ca3e-4575-48c4-9647-d77b96b331ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but PolynomialFeatures was fitted with feature names\n",
      "  warnings.warn(\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:1474: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1300: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but SVR was fitted with feature names\n",
      "  warnings.warn(\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:668: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)  # TODO: Is this still required?\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\27855\\AppData\\Local\\Temp\\ipykernel_22740\\2990842660.py:44: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from font(s) SimHei.\n",
      "  plt.tight_layout()\n",
      "D:\\Program Files\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from font(s) SimHei.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型性能对比:\n",
      "------------------------------------------------------------\n",
      "模型名称            均方误差(MSE)       R平方(R²)        \n",
      "------------------------------------------------------------\n",
      "一元线性回归          7121839.87      0.8551         \n",
      "三次多项式回归         2650426.34      0.9461         \n",
      "决策树回归           1615240.46      0.9671         \n",
      "随机森林回归          959750.50       0.9805         \n",
      "支持向量回归          27409216.12     0.4425         \n",
      "梯度提升回归          923581.89       0.9812         \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "x_test = np.linspace(0, 1.2, 100).reshape(-1, 1)\n",
    "models = {\n",
    "    \"一元线性回归\": LinearRegression(),\n",
    "    \"三次多项式回归\": None,\n",
    "    \"决策树回归\": DecisionTreeRegressor(max_depth=3),\n",
    "    \"随机森林回归\": RandomForestRegressor(n_estimators=100, random_state=42),\n",
    "    \"支持向量回归\": SVR(kernel='rbf', C=100, gamma=0.1),\n",
    "    \"梯度提升回归\": GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42)\n",
    "}\n",
    "results = {}\n",
    "for name, model in models.items():\n",
    "    if name == \"三次多项式回归\":\n",
    "        poly = PolynomialFeatures(degree=3)\n",
    "        x_poly = poly.fit_transform(x) \n",
    "        x_test_poly = poly.transform(x_test)\n",
    "        model = LinearRegression().fit(x_poly, y)  \n",
    "        y_pred = model.predict(x_test_poly)\n",
    "        mse = mean_squared_error(y, model.predict(x_poly)) \n",
    "        r2 = r2_score(y, model.predict(x_poly))\n",
    "    else:\n",
    "        model.fit(x, y)\n",
    "        y_pred = model.predict(x_test)\n",
    "        mse = mean_squared_error(y, model.predict(x))  \n",
    "        r2 = r2_score(y, model.predict(x))\n",
    "    results[name] = {\"MSE\": mse, \"R²\": r2, \"model\": model, \"x_test\": x_test, \"y_pred\": y_pred}\n",
    "plt.figure(figsize=(18, 12))\n",
    "for i, (name, result) in enumerate(results.items()):\n",
    "    plt.subplot(3, 2, i+1)  \n",
    "    plt.scatter(x, y, color='blue', label='实际数据')  # 使用小写的 x 和 y\n",
    "    plt.plot(result[\"x_test\"], result[\"y_pred\"], color='red', label=name)\n",
    "    plt.title(f\"{name}\\nMSE: {result['MSE']:.2f}, R²: {result['R²']:.4f}\")\n",
    "    plt.xlabel('工龄')\n",
    "    plt.ylabel('薪水')\n",
    "    plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(\"模型性能对比:\")\n",
    "print(\"-\" * 60)\n",
    "print(f\"{'模型名称':<15} {'均方误差(MSE)':<15} {'R平方(R²)':<15}\")\n",
    "print(\"-\" * 60)\n",
    "for name, result in results.items():\n",
    "    print(f\"{name:<15} {result['MSE']:<15.2f} {result['R²']:<15.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fbfcc2d-b52a-48f3-80f0-b9bd7b3888ee",
   "metadata": {},
   "source": [
    "从模型性能对比来看，梯度提升回归表现最优（MSE最小、R²最高），其次是随机森林回归和决策树回归；一元线性回归因无法捕捉非线性关系表现较弱，支持向量回归则因参数适配问题效果最差。具体结论如下：\r\n",
    "\r\n",
    "**最佳模型**：梯度提升回归（MSE=923581.89，R²=0.9812），拟合效果最贴近实际数据。\r\n",
    "\r\n",
    "**次优模型**：随机森林回归（MSE=959750.50，R²=0.9805），稳定性与精度兼具。\r\n",
    "\r\n",
    "**中等模型**：决策树回归（MSE=1615240.46，R²=0.9671），能捕捉分段趋势但略逊于集成模型。\r\n",
    "\r\n",
    "**一般模型**：三次多项式回归（MSE=2650426.34，R²=0.9461），优于线性模型但不及树模型。\r\n",
    "\r\n",
    "**最差模型**：支持向量回归（MSE=27409216.12，R²=0.4425），参数未充分适配数据特性。\r\n",
    "\r\n",
    "整体而言，非线性模型（尤其是梯度提升、随机森林）更适合拟合IT行业收入与工龄的非线性关系，而简单线性模型效果有限。模型效果有限。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f08d870-4f70-49e8-becd-1381d9422a16",
   "metadata": {},
   "source": [
    "## 案例实战2：客户价值预测模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eaee050-b3df-4429-9633-e0e3aeb8e917",
   "metadata": {},
   "source": [
    "### 读取数据，显示前五行的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c1329ba-9a5b-405f-b0f8-06312cbde272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>客户价值</th>\n",
       "      <th>历史贷款金额</th>\n",
       "      <th>贷款次数</th>\n",
       "      <th>学历</th>\n",
       "      <th>月收入</th>\n",
       "      <th>性别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1150</td>\n",
       "      <td>6488</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9567</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1157</td>\n",
       "      <td>5194</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10767</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1163</td>\n",
       "      <td>7066</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9317</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>983</td>\n",
       "      <td>3550</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10517</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1205</td>\n",
       "      <td>7847</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>11267</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   客户价值  历史贷款金额  贷款次数  学历    月收入  性别\n",
       "0  1150    6488     2   2   9567   1\n",
       "1  1157    5194     4   2  10767   0\n",
       "2  1163    7066     3   2   9317   0\n",
       "3   983    3550     3   2  10517   0\n",
       "4  1205    7847     3   3  11267   1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "a=pandas.read_excel('客户价值数据表.xlsx')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c349e34-3b7a-4e9f-86a8-3868c2dd9339",
   "metadata": {},
   "source": [
    "### 进行自变量和因变量的选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19d15e78-d456-4030-a001-9989df266cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=a[['历史贷款金额','贷款次数','学历','月收入','性别']]\n",
    "Y=a['客户价值']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "474b4b4f-85fa-414b-a27b-695a20f3793a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "b=LinearRegression()\n",
    "b.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7da946a-e212-43f7-bf4c-bab92469b00e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数：[5.71421731e-02 9.61723492e+01 1.13452022e+02 5.61326459e-02\n",
      " 1.97874093e+00]\n",
      "常数项系数k0为：-208.42004079958383\n"
     ]
    }
   ],
   "source": [
    "print('系数：'+str(b.coef_))\n",
    "print('常数项系数k0为：'+str(b.intercept_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45234550-d8bb-4e6a-b1f4-228c9adc6371",
   "metadata": {},
   "source": [
    "### 模型评估："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a6c7409-cbdb-48e9-a54a-5417a86183c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   客户价值   R-squared:                       0.571\n",
      "Model:                            OLS   Adj. R-squared:                  0.553\n",
      "Method:                 Least Squares   F-statistic:                     32.44\n",
      "Date:                Tue, 23 Sep 2025   Prob (F-statistic):           6.41e-21\n",
      "Time:                        15:13:47   Log-Likelihood:                -843.50\n",
      "No. Observations:                 128   AIC:                             1699.\n",
      "Df Residuals:                     122   BIC:                             1716.\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       -208.4200    163.810     -1.272      0.206    -532.699     115.859\n",
      "历史贷款金额         0.0571      0.010      5.945      0.000       0.038       0.076\n",
      "贷款次数          96.1723     25.962      3.704      0.000      44.778     147.567\n",
      "学历           113.4520     37.909      2.993      0.003      38.406     188.498\n",
      "月收入            0.0561      0.019      2.941      0.004       0.018       0.094\n",
      "性别             1.9787     32.286      0.061      0.951     -61.934      65.891\n",
      "==============================================================================\n",
      "Omnibus:                        1.597   Durbin-Watson:                   2.155\n",
      "Prob(Omnibus):                  0.450   Jarque-Bera (JB):                1.538\n",
      "Skew:                           0.264   Prob(JB):                        0.464\n",
      "Kurtosis:                       2.900   Cond. No.                     1.28e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.28e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X2=sm.add_constant(X)\n",
    "est=sm.OLS(Y,X2).fit()\n",
    "print(est.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e55b67e-c058-4302-8dcf-6c052931eb1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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